SoroushVahidi/scidocs huggingface dataset:
"My Data Just Goes Everywhere: " User Mental Models of the Internet and Implications for Privacy and Security
(2015)

Many people use the Internet every day yet know little about how it really works. Prior literature diverges on how people’s Internet knowledge affects their privacy and security decisions. We undertook a qualitative study to understand what people do and do not know about the Internet and how that knowledge affects their responses to privacy and security risks. Lay people, as compared to those with computer science or related backgrounds, had simpler mental models that omitted Internet levels, organizations, and entities. People with more articulated technical models perceived more privacy threats, possibly driven by their more accurate understanding of where specific risks could occur in the network. Despite these differences, we did not find a direct relationship between people’s technical background and the actions they took to control their privacy or increase their security online. Consistent with other work on user knowledge and experience, our study suggests a greater emphasis on policies and systems that protect privacy and security without relying too much on users’ security practices.
Thirty-Five Years of Research on Neuro-Linguistic Programming . NLP Research Data Base . State of the Art or Pseudoscientific Decoration ?
(2010)

The huge popularity of Neuro-Linguistic Programming (NLP) therapies and training has not been accompanied by knowledge of the empirical underpinnings of the concept. The article presents the concept of NLP in the light of empirical research in the Neuro-Linguistic Programming Research Data Base. From among 315 articles the author selected 63 studies published in journals from the Master Journal List of ISI. Out of 33 studies, 18.2% show results supporting the tenets of NLP, 54.5% results non-supportive of the NLP tenets and 27.3% brings uncertain results. The qualitative analysis indicates the greater weight of the non-supportive studies and their greater methodological worth against the ones supporting the tenets. Results contradict the claim of an empirical basis of NLP.
Cloud Computing: A Review
(2012)

Cloud computing is a development of parallel, distributed and grid computing which provides computing potential as a service to clients rather than a product. Clients can access software resources, valuable information and hardware devices as a subscribed and monitored service over a network through cloud computing.Due to large number of requests for access to resources and service level agreements between cloud service providers and clients, few burning issues in cloud environment like QoS, Power, Privacy and Security, VM Migration, Resource Allocation and Scheduling need attention of research community.Resource allocation among multiple clients has to be ensured as per service level agreements. Several techniques have been invented and tested by research community for generation of optimal schedules in cloud computing. A few promising approaches like Metaheuristics, Greedy, Heuristic technique and Genetic are applied for task scheduling in several parallel and distributed systems. This paper presents a review on scheduling proposals in cloud environment.
Patient satisfaction and medication adherence assessment amongst patients at the diabetes medication therapy adherence clinic.
(2016)

AIMS
To determine the satisfaction and current adherence status of patients with diabetes mellitus at the diabetes Medication Therapy Adherence Clinic and the relationship between patient satisfaction and adherence.


METHODS
This cross-sectional descriptive study was carried out at three government hospitals in the state of Johor, Malaysia. Patient's satisfaction was measured using the Patient Satisfaction with Pharmaceutical Care Questionnaire; medication adherence was measured using the eight-item Morisky Medication Adherence Scale.


RESULTS
Of n=165 patients, 87.0% of patients were satisfied with DMTAC service (score 60-100) with mean scores of 76.8. On the basis of MMAS, 29.1% had a medium rate and 26.1% had a high rate of adherence. Females are 3.02 times more satisfied with the pharmaceutical service compared to males (OR 3.03, 95% CI 1.12-8.24, p<0.05) and non-Malays are less satisfied with pharmaceutical care provided during DMTAC compared to Malays (OR 0.32, 95% CI 0.12-0.85, p<0.05). Older patients age group ≥60 years were 3.29 times more likely to adhere to their medications (OR 3.29, 95% CI 1.10-9.86, p<0.05). Females were the most adherent compared to males (OR 2.33, 95%CI 1.10-4.93, p<0.05) and patients with secondary level of education were 2.72 times more adherent to their medications compared to those in primary school and no formal education (OR 2.72, 95%CI 1.13-6.55, p<0.05). There is a significant (p<0.01), positive fair correlation (r=0.377) between satisfaction and adherence.


CONCLUSION
Patients were highly satisfied with DMTAC service, while their adherence levels were low. There is an association between patient satisfaction and adherence.
Wind Turbine Structural Health Monitoring : A Short Investigation Based on SCADA Data
(2015)

The use of offshore wind farms has been growing in recent years, as steadier and higher wind speeds can be generally found over water compared to land. Moreover, as human activities tend to complicate the construction of land wind farms, offshore locations, which can be found more easily near densely populated areas, can be seen as an attractive choice. However, the cost of an offshore wind farm is relatively high, and therefore their reliability is crucial if they ever need to be fully integrated into the energy arena. As wind turbines have become more complex, efficient, and expensive structures, they require more sophisticated monitoring systems, especially in offshore sites where the financial losses due to failure could be substantial. This paper presents the preliminary analysis of supervisor control and data acquisition (SCADA) extracts from the Lillgrund wind farm for the purposes of structural health monitoring. A machine learning approach is applied in order to produce individual power curves, and then predict measurements of the power produced of each wind turbine from the measurements of the other wind turbines in the farm. A comparison between neural network and Gaussian process regression is also made.
Deep Residual Learning for Image Recognition
(2016)

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Learning a Deep Convolutional Network for Image Super-Resolution
(2014)

We propose a deep learning method for single image superresolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) [15] that takes the lowresolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage.
A Public Domain Dataset for Human Activity Recognition using Smartphones
(2013)

Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. One of the most recent, challenging and appealing applications in this framework consists in sensing human body motion using smartphones to gather context information about people actions. In this context, we describe in this work an Activity Recognition database, built from the recordings of 30 subjects doing Activities of Daily Living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors, which is released to public domain on a well-known on-line repository. Results, obtained on the dataset by exploiting a multiclass Support Vector Machine (SVM), are also acknowledged.
On deep learning-based channel decoding
(2017)

We revisit the idea of using deep neural networks for one-shot decoding of random and structured codes, such as polar codes. Although it is possible to achieve maximum a posteriori (MAP) bit error rate (BER) performance for both code families and for short codeword lengths, we observe that (i) structured codes are easier to learn and (ii) the neural network is able to generalize to codewords that it has never seen during training for structured, but not for random codes. These results provide some evidence that neural networks can learn a form of decoding algorithm, rather than only a simple classifier. We introduce the metric normalized validation error (NVE) in order to further investigate the potential and limitations of deep learning-based decoding with respect to performance and complexity.
Automated plant Watering system
(2016)

In daily operations related to farming or gardening Watering is the most important cultural practice and the most labor-intensive task. No matter whichever weather it is, either too hot and dry or too cloudy and wet, you want to be able to control the amount of water that reaches your plants. Modern watering systems could be effectively used to water plants when they need it. But this manual process of watering requires two important aspects to be considered: when and how much to water. In order to replace manual activities and making gardener's work easier, we have create automatic plant watering system. By adding automated plant watering system to your garden or agricultural field, you will help all of your plants reach their fullest potential as well as conserving water. Using sprinklers drip emitters, or a combination of both, we can design a system that is ideal for every plant in our yard. For implementation of automatic plant watering system, we have used combination of sprinkler systems, pipes, and nozzles. This project uses the ATmega328 microcontroller. It is programmed to sense moisture level of plants at particular instance of time, if the moisture content is less than specified threshold which is predefined according to particular plant's water need then desired amount of water is supplied till it reaches threshold. Generally, plants need to be watered twice a day, morning and evening. Thus, the microcontroller is programmed to water plants two times per day. System is designed in such a way that it reports its current state as well as remind the user to add water to the tank. All this notifications are made through mobile application. We hope that through this prototype we all can enjoy having plants, without being worried about absent or forgetfulness.
Energy-Efficient I/O Thread Schedulers for NVMe SSDs on NUMA
(2017)

Non-volatile memory express (NVMe) based SSDs and the NUMA platform are widely adopted in servers to achieve faster storage speed and more powerful processing capability. As of now, very little research has been conducted to investigate the performance and energy efficiency of the state-of-the-art NUMA architecture integrated with NVMe SSDs, an emerging technology used to host parallel I/O threads. As this technology continues to be widely developed and adopted, we need to understand the runtime behaviors of such systems in order to design software runtime systems that deliver optimal performance while consuming only the necessary amount of energy. This paper characterizes the runtime behaviors of a Linux-based NUMA system employing multiple NVMe SSDs. Our comprehensive performance and energy-efficiency study using massive numbers of parallel I/O threads shows that the penalty due to CPU contention is much smaller than that due to remote access of NVMe SSDs. Based on this insight, we develop a dynamic "lesser evil" algorithm called ESN, to minimize the impact of these two types of penalties. ESN is an energy-efficient profiling-based I/O thread scheduler for managing I/O threads accessing NVMe SSDs on NUMA systems. Our empirical evaluation shows that ESN can achieve optimal I/O throughput and latency while consuming up to 50% less energy and using fewer CPUs.
Cascade Mask Generation Framework for Fast Small Object Detection
(2018)

Detecting small objects is a challenging task. Existing CNN-based objection detection pipeline faces such a dilemma: using a high-resolution image as input incurs high computational cost, but using a low-resolution image as input loses the feature representation of small objects and therefore leads to low accuracy. In this work, we propose a cascade mask generation framework to tackle this issue. The proposed framework takes in multi-scale images as input and processes them in ascending order of the scale. Each processing stage outputs object proposals as well as a region-of-interest (RoI) mask for the next stage. With RoI convolution, the masked regions can be excluded from the computation in the next stage. The procedure continues until the largest scale image is processed. Finally, the object proposals generated from multiple scales are classified by a post classifier. Extensive experiments on Tsinghua-Tencent 100K traffic sign benchmark demonstrate that our approach achieves state-of-the-art small object detection performance at a significantly improved speed-accuracy tradeoff compared with previous methods.
Broadband Bent Triangular Omnidirectional Antenna for RF Energy Harvesting
(2016)

In this letter, a broadband bent triangular omnidirectional antenna is presented for RF energy harvesting. The antenna has a bandwidth for VSWR ≤ 2 from 850 MHz to 1.94 GHz. The antenna is designed to receive both horizontal and vertical polarized waves and has a stable radiation pattern over the entire bandwidth. Antenna has also been optimized for energy harvesting application and it is designed for 100 Ω input impedance to provide a passive voltage amplification and impedance matching to the rectifier. A peak efficiency of 60% and 17% is obtained for a load of 500 Ω at 980 and 1800 MHz, respectively. At a cell site while harvesting all bands simultaneously a voltage of 3.76 V for open circuit and 1.38 V across a load of 4.3 k Ω is obtained at a distance of 25 m using an array of two elements of the rectenna.
Graph Indexing: Tree + Delta >= Graph
(2007)

Recent scientific and technological advances have witnessed an abundance of structural patterns modeled as graphs. As a result, it is of special interest to process graph containment queries effectively on large graph databases. Given a graph database G, and a query graph q, the graph containment query is to retrieve all graphs in G which contain q as subgraph(s). Due to the vast number of graphs in G and the nature of complexity for subgraph isomorphism testing, it is desirable to make use of high-quality graph indexing mechanisms to reduce the overall query processing cost. In this paper, we propose a new cost-effective graph indexing method based on frequent tree-features of the graph database. We analyze the effectiveness and efficiency of tree as indexing feature from three critical aspects: feature size, feature selection cost, and pruning power. In order to achieve better pruning ability than existing graph-based indexing methods, we select, in addition to frequent tree-features (Tree), a small number of discriminative graphs (∆) on demand, without a costly graph mining process beforehand. Our study verifies that (Tree+∆) is a better choice than graph for indexing purpose, denoted (Tree+∆ ≥Graph), to address the graph containment query problem. It has two implications: (1) the index construction by (Tree+∆) is efficient, and (2) the graph containment query processing by (Tree+∆) is efficient. Our experimental studies demonstrate that (Tree+∆) has a compact index structure, achieves an order of magnitude better performance in index construction, and most importantly, outperforms up-to-date graphbased indexing methods: gIndex and C-Tree, in graph containment query processing.
Deep learning based human action recognition: A survey
(2017)

Human action recognition has attracted much attentions because of its great potential applications. With the rapid development of computer performance and Internet, the methods of human action recognition based on deep learning become mainstream and develop at a breathless pace. This paper will give a novel reasonable taxonomy and a review of deep learning human action recognition methods based on color videos, skeleton sequences and depth maps. In addition, some datasets and effective tricks in action recognition deep learning methods will be introduced, and also the development trend is discussed.
A compact dual-band (L1/L2) GPS antenna design
(2012)

A small slot-loaded patch antenna design developed for receiving both L1 and L2 bands GPS signals is discussed. The dual band coverage is achieved by using a patch mode at L2 band and a slot mode at L1 band. High dielectric material and meandered slot line are employed to reduce the antenna size down to 25.4 mm in diameter. The RHCP is achieved by combining two orthogonal modes via a small 0°-90° hybrid chip. Both patch and slot modes share a single proximity probe conveniently located on the side of the antenna (Fig.1). This paper discusses about the design procedure as well as simulated antenna performance.
PROBABILISTIC NEURAL NETWORK
(2013)

This paper reports results of artificial neural network for robot navigation tasks. Machine learning methods have proven usability in many complex problems concerning mobile robots control. In particular we deal with the well-known strategy of navigating by “wall-following”. In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks. The PNN result was compared with the results of the Logistic Perceptron, Multilayer Perceptron, Mixture of Experts and Elman neural networks and the results of the previous studies reported focusing on robot navigation tasks and using same dataset. It was observed the PNN is the best classification accuracy with 99,635% accuracy using same dataset.
Trends and Challenges in CMOS Design for Emerging 60 GHz WPAN Applications
(2011)

The extensive growth of wireless communications industry is creating a big market opportunity. Wireless operators are currently searching for new solutions which would be implemented into the existing wireless communication networks to provide the broader bandwidth, the better quality and new value-added services. In the last decade, most commercial efforts were focused on the 1-10 GHz spectrum for voice and data applications for mobile phones and portable computers (Niknejad & Hashemi, 2008). Nowadays, the interest is growing in applications that use high rate wireless communications. Multigigabit-per-second communication requires a very large bandwidth. The Ultra-Wide Band (UWB) technology was basically used for this issue. However, this technology has some shortcomings including problems with interference and a limited data rate. Furthermore, the 3–5 GHz spectrum is relatively crowded with many interferers appearing in the WiFi bands (Niknejad & Hashemi, 2008). The use of millimeter wave frequency band is considered the most promising technology for broadband wireless. In 2001, the Federal Communications Commission (FCC) released a set of rules governing the use of spectrum between 57 and 66 GHz (Baldwin, 2007). Hence, a large bandwidth coupled with high allowable transmit power equals high possible data rates. Traditionally the implementation of 60 GHz radio technology required expensive technologies based on III-V compound semiconductors such as InP and GaAs (Smulders et al., 2007). The rapid progress of CMOS technology has enabled its application in millimeter wave applications. Currently, the transistors became small enough, consequently fast enough. As a result, the CMOS technology has become one of the most attractive choices in implementing 60 GHz radio due to its low cost and high level of integration (Doan et al., 2005). Despite the advantages of CMOS technology, the design of 60 GHz CMOS transceiver exhibits several challenges and difficulties that the designers must overcome. This chapter aims to explore the potential of the 60 GHz band in the use for emergent generation multi-gigabit wireless applications. The chapter presents a quick overview of the state-of-the-art of 60 GHz radio technology and its potentials to provide for high data rate and short range wireless communications. The chapter is organized as follows. Section 2 presents an overview about 60 GHz band. The advantages are presented to highlight the performance characteristics of this band. The opportunities of the physical layer of the IEEE
Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system
(2015)

Advances in bio-telemetry technology have made it possible to automatically monitor and classify behavioural activities in many animals, including domesticated species such as dairy cows. Automated behavioural classification has the potential to improve health and welfare monitoring processes as part of a Precision Livestock Farming approach. Recent studies have used accelerometers and pedometers to classify behavioural activities in dairy cows, but such approaches often cannot discriminate accurately between biologically important behaviours such as feeding, lying and standing or transition events between lying and standing. In this study we develop a decision-tree algorithm that uses tri-axial accelerometer data from a neck-mounted sensor to both classify biologically important behaviour in dairy cows and to detect transition events between lying and standing. Data were collected from six dairy cows that were monitored continuously for 36 h. Direct visual observations of each cow were used to validate the algorithm. Results show that the decision-tree algorithm is able to accurately classify three types of biologically relevant behaviours: lying (77.42 % sensitivity, 98.63 % precision), standing (88.00 % sensitivity, 55.00 % precision), and feeding (98.78 % sensitivity, 93.10 % precision). Transitions between standing and lying were also detected accurately with an average sensitivity of 96.45 % and an average precision of 87.50 %. The sensitivity and precision of the decision-tree algorithm matches the performance of more computationally intensive algorithms such as hidden Markov models and support vector machines. Biologically important behavioural activities in housed dairy cows can be classified accurately using a simple decision-tree algorithm applied to data collected from a neck-mounted tri-axial accelerometer. The algorithm could form part of a real-time behavioural monitoring system in order to automatically detect dairy cow health and welfare status.